143 lines
5.6 KiB
Python
143 lines
5.6 KiB
Python
# -*- coding:utf-8 -*-
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# @Author len
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# @Create 2023/11/30 11:08
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import sensor, lcd, time
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from machine import Timer, PWM
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lcd.init(freq=15000000) # 初始化LCD
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sensor.reset() # 复位和初始化摄像头
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sensor.set_vflip(1) # 将摄像头设置成后置方式（所见即所得）
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sensor.set_pixformat(sensor.RGB565)# 设置像素格式为彩色 RGB565
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sensor.set_framesize(sensor.QVGA) # 设置帧大小为 QVGA (320x240)
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sensor.set_auto_gain(False)
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sensor.set_auto_whitebal(False)
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sensor.set_auto_gain(0, 0)
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sensor.skip_frames(time=200) # 等待设置生效
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def draw_circles():
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"""
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检测并绘制图像中的圆形，仅在图像的右半部分进行检测，
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"""
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start_time = time.ticks_ms() # 记录开始时间（毫秒）
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def circles_overlap(c1, c2):
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"""判断两个圆是否重叠"""
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dx = c1.x() - c2.x()
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dy = c1.y() - c2.y()
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distance = (dx**2 + dy**2)**0.5
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return distance < (c1.r() + c2.r())
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def merge_circles(new_circle, cache):
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"""尝试合并新检测到的圆与缓存中的圆"""
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for existing in cache:
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if circles_overlap(new_circle, existing):
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return False # 如果重叠，不加入新的圆
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cache.append(new_circle) # 如果没有重叠，添加到缓存中
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return True
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def calculate_lab(area, img):
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"""计算给定区域的平均 LAB 值"""
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stats = img.get_statistics(roi=area)
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return stats.l_mean(), stats.a_mean(), stats.b_mean()
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def determine_color_from_lab(lab):
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"""
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返回最接近的颜色以及对应的距离
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:param lab: (l, a, b) 三元组
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:return: (closest_color, min_distance)
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"""
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# 你可以根据实际情况添加或修改更多预设颜色及其 LAB
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predefined_colors = {
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"红色": (50, 38, 1),
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"黄色": (86, 2, 27),
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"绿色": (75, -29, 15)
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}
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min_distance = float('inf')
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closest_color = "灰色" # 默认或者未匹配时
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# 打印一下实际检测到的LAB，便于调试
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# print("检测到的LAB:", lab)
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for color_name, (l_ref, a_ref, b_ref) in predefined_colors.items():
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distance = ((lab[0] - l_ref) ** 2 +
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(lab[1] - a_ref) ** 2 +
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(lab[2] - b_ref) ** 2) ** 0.5
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if distance < min_distance:
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min_distance = distance
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closest_color = color_name
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if min_distance < 10:
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print("最接近的颜色={}, 距离={:.2f}".format(closest_color, min_distance))
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# print("最接近的颜色={}, 距离={:.2f}".format(closest_color, min_distance))
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return closest_color, min_distance
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count=0
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min_color='red'
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min_distance=50
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while True:
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img = sensor.snapshot()
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# 只处理右半部分
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roi = (img.width() // 2, 0, img.width() // 2, img.height())
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# 在此根据整体亮度简单调参，阈值可自行调整
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threshold = max(1000, min(5000, int(img.get_statistics().l_mean() * 50)))
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# find_circles 参数可根据实际效果调整
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circles = img.find_circles(roi=roi,
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threshold=2500,
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x_margin=20,
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y_margin=20,
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r_margin=20,
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r_min=15,
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r_max=30,
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r_step=2)
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if circles:
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closest_circle = None
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closest_distance = float('inf')
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closest_color = "未知"
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for c in circles:
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area = (c.x() - c.r(), c.y() - c.r(), 2*c.r(), 2*c.r())
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lab_val = calculate_lab(area, img)
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color_name, distance = determine_color_from_lab(lab_val)
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# 找出最小距离（与预设颜色最相似）的那个圆
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if distance < closest_distance:
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closest_distance = distance
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closest_color = color_name
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closest_circle = c
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if closest_circle is None:
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print('没有识别到')
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continue
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elif closest_distance<20:
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area = (closest_circle.x() - closest_circle.r(),
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closest_circle.y() - closest_circle.r(),
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2*closest_circle.r(), 2*closest_circle.r())
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# 画框和画圆
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img.draw_rectangle(area, color=(255, 0, 0))
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img.draw_circle(closest_circle.x(), closest_circle.y(), closest_circle.r(), color=(255, 0, 0))
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print("最终识别到的颜色={}, 距离={:.2f}".format(closest_color, closest_distance))
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min_distance=50
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print("{}轮检测耗时: {} ms".format(count,time.ticks_diff(time.ticks_ms(), start_time)))
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start_time = time.ticks_ms()
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count+=1
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else:
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if closest_distance<min_distance:
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min_color=closest_color
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min_distance=closest_distance
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if time.ticks_diff(time.ticks_ms(), start_time) > 5000:
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print("最终识别到的颜色={}, 距离={:.2f}".format(min_color, min_distance))
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print("{}轮检测耗时: {} ms".format(count,time.ticks_diff(time.ticks_ms(), start_time)))
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start_time = time.ticks_ms()
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min_distance=50
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count+=1
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# 主函数调用
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if __name__ == "__main__":
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draw_circles()
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